It seems to me that AIs have remained stubbornly terrible at this from GPT-3 to GPT-4 to Sonnet 3.5.1 to o1[2]; that the improvement on this hard-to-specify quality has been ~0. I guess we’ll see if o3 (or an o-series model based on the next-generation base model) change that. AI does feel right on the cusp of getting good at this...
… just as it felt at the time of GPT-3.5, and GPT-4, and Sonnet 3.5.1, and o1. That just the slightest improvement along this axis would allow us to plug the outputs of AI cognition into its inputs and get a competent, autonomous AI agent.
Boy do I disagree with this take! Excited to discuss.
Can you say more about what skills you think the GPT series has shown ~0 improvement on?
Because if it’s “competent, autonomous agency” then there has been massive progress over the last two years and over the last few months in particular. METR has basically spent dozens of FTE-years specifically trying to measure progress in autonomous agency capability, both with formal benchmarks and with lots of high-surface-area interaction with models (they have people building scaffolds to make the AIs into agents and do various tasks etc.) And METR seems to think that progress has been rapid and indeed faster than they expected.
Has there been enough progress to automate swathes of jobs? No, of course not—see the benchmarks. E.g. RE-bench shows that even the best public models like o1 and newsonnet are only as good as professional coders on time horizons of, like, an hour or so. (give or take, depends on how you measure, the task, etc.) Which means that if you give them the sort of task that would take a normal employee, like, three hours, they are worse than a competent human professional. Specifically they’d burn lots of tokens and compute and push lots of buggy code and overall make a mess of things, just like an eager but incompetent employee.
And I’d say the models are unusually good at these coding tasks compared to other kinds of useful professional tasks, because the companies have been trying harder to train them to code and it’s inherently easier to train due to faster feedback loops etc.
Can you say more about what skills you think the GPT series has shown ~0 improvement on?
Alright, let’s try this. But this is going to be vague.
Here’s a cluster of things that SotA AIs seem stubbornly bad at:
Innovation. LLMs are perfectly able to understand an innovative idea if it’s described to them, even if it’s a new idea that was produced after their knowledge-cutoff date. Yet, there hasn’t been a single LLM-originating innovation, and all attempts to design “AI scientists” have produced useless slop. They seem to have terrible “research taste”, even though they should be able to learn this implicit skill from the training data.
Reliability. Humans are very reliable agents, and SotA AIs aren’t, even when e. g. put into wrappers that encourage them to sanity-check their work. The gap in reliability seems qualitative, rather than just quantitative.
Solving non-templated problems. There seems to be a bimodal distribution of a sort, where some people report LLMs producing excellent code/math, and others report that they fail basic tasks.
Compounding returns on problem-solving time. As the graph you provided shows, humans’ performance scales dramatically with the time they spent on the problem, whereas AIs’ – even o1′s – doesn’t.
My sense is that LLMs are missing some sort of “self-steering” “true autonomy” quality; the quality that allows humans to:
Stare at the actual problem they’re solving, and build its highly detailed model in a “bottom-up” manner. Instead, LLMs go “top-down”: they retrieve the closest-match template problem from a vast database, fill-in some details, and solve that problem.
(Non-templatedness/fluid intelligence.)
Iteratively improve their model of a problem over the course of problem-solving, and do sophisticated course-correction if they realize their strategy isn’t working or if they’re solving the wrong problem. Humans can “snap out of it” if they realize they’re messing up, instead of just doing what they’re doing on inertia.
(Reliability.)
Recognize when their model of a given problem represents a nontrivially new “template” that can be memorized and applied in a variety of other situations, and what these situations might be.
(Innovation.)
My model is that all LLM progress so far has involved making LLMs better at the “top-down” thing. They end up with increasingly bigger databases of template problems, the closest-match templates end up ever-closer to the actual problems they’re facing, their ability to fill-in the details becomes ever-richer, etc. This improves their zero-shot skills, and test-time compute scaling allows them to “feel out” the problem’s shape over an extended period and find an ever-more-detailed top-down fit.
But it’s still fundamentally not what humans do. Humans are able to instantiate a completely new abstract model of a problem – even if it’s initially based on a stored template – and chisel at it until it matches the actual problem near-perfectly. This allows them to be much more reliable; this allows them to keep themselves on-track; this allows them to find “genuinely new” innovations.
The two methods do ultimately converge to the same end result: in the limit of a sufficiently expressive template-database, LLMs would be able to attain the same level of reliability/problem-representation-accuracy as humans. But the top-down method of approaching this limit seems ruinously computationally inefficient; perhaps so inefficient it saturates around GPT-4′s capability level.[1]
LLMs are sleep-walking. We can make their dreams ever-closer to reality, and that makes the illusion that they’re awake ever-stronger. But they’re not, and the current approaches may not be able to wake them up at all.
(As an abstract analogy: imagine that you need to color the space bounded by some 2D curve. In one case, you can take a pencil and do it directly. In another case, you have a collection of cutouts of geometric figures, and you have to fill the area by assembling a collage. If you have a sufficiently rich collection of figures, you can come arbitrarily close; but the “bottom-up” approach is strictly better. In particular, it can handle arbitrarily complicated shapes out-of-the-box, whereas the second approach would require dramatically bigger collections the more complicated the shapes get.)
Edit: Or so my current “bearish on LLMs” model goes. The performance of o3 or GPT-5/6 can very much break it, and the actual mechanisms described are necessarily speculative and tentative.
Under this toy model, it needn’t have saturated around this level; it could’ve comfortably overshot human capabilities. But this doesn’t seem to be what’s happening, likely due to some limitation of the current paradigm not covered by this model.
Thanks! Time will tell who is right. Point by point reply:
You list four things AIs seem stubbornly bad at: 1. Innovation. 2. Reliability. 3. Solving non-templated problems. 4. Compounding returns on problem-solving-time.
First of all, 2 and 4 seem closely related to me. I would say: “Agency skills” are the skills key to being an effective agent, i.e. skills useful for operating autonomously for long periods in pursuit of goals. Noticing when you are stuck is a simple example of an agency skill. Planning is another simple example. In-context learning is another example. would say that current AIs lack agency skills, and that 2 and 4 are just special cases of this. I would also venture to guess with less confidence that 1 and 3 might be because of this as well—perhaps the reason AIs haven’t made any truly novel innovations yet is that doing so takes intellectual work, work they can’t do because they can’t operate autonomously for long periods in pursuit of goals. (Note that reasoning models like o1 are a big leap in the direction of being able to do this!) And perhaps the reason behind the relatively poor performance on non-templated tasks is… wait actually no, that one has a very easy separate explanation, which is that they’ve been trained less on those tasks. A human, too, is better at stuff they’ve done a lot.
Secondly, and more importantly, I don’t think we can say there has been ~0 progress on these dimensions in the last few years, whether you conceive of them in your way or my way. Progress is in general s-curvy; adoption curves are s-curvy. Suppose for example that GPT2 was 4 SDs worse than average human at innovation, reliability, etc. and GPT3 was 3 SDs worse and GPT4 was 2 SDs worse and o1 is 1 SD worse. Under this supposition, the world would look the way that it looks today—Thane would notice zero novel innovations from AIs, Thane would have friends who try to use o1 for coding and find that it’s not useful without templates, etc. Meanwhile, as I’m sure you are aware pretty much every benchmark anyone has ever made has shown rapid progress in the last few years—including benchmarks made by METR who was specifically trying to measure AI R&D ability and agency abilities, and which genuinely do seem to require (small) amounts of agency. So I think the balance of evidence is in favor of progress on the dimensions you are talking about—it just hasn’t reached human level yet, or at any rate not the level at which you’d notice big exciting changes in the world. (Analogous to: Suppose we’ve measured COVID in some countries but not others, and found that in every country we’ve measured, COVID has spread to about 0.01% − 0.001% of the population, and is growing exponentially. If we live in a country that hasn’t measured yet, we should assume COVID is spreading even though we don’t know anyone personally who is sick yet.)
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You say:
My model is that all LLM progress so far has involved making LLMs better at the “top-down” thing. They end up with increasingly bigger databases of template problems, the closest-match templates end up ever-closer to the actual problems they’re facing, their ability to fill-in the details becomes ever-richer, etc. This improves their zero-shot skills, and test-time compute scaling allows them to “feel out” the problem’s shape over an extended period and find an ever-more-detailed top-down fit.
But it’s still fundamentally not what humans do. Humans are able to instantiate a completely new abstract model of a problem – even if it’s initially based on a stored template – and chisel at it until it matches the actual problem near-perfectly. This allows them to be much more reliable; this allows them to keep themselves on-track; this allows them to find “genuinely new” innovations.
Top down vs. bottom-up seem like two different ways of solving intellectual problems. Do you think it’s a sharp binary distinction? Or do you think it’s a spectrum? If the latter, what makes you think o1 isn’t farther along the spectrum than GPT3? If the former—if it’s a sharp binary—can you say what it is about LLM architecture and/or training methods that renders them incapable of thinking in the bottom-up way? (Like, naively it seems like o1 can do sophisticated reasoning. Moreover, it seems like it was trained in a way that would incentivize it to learn skills useful for solving math problems, and ‘bottom-up reasoning’ seems like a skill that would be useful. Why wouldn’t it learn it?)
Can you describe an intellectual or practical feat, or ideally a problem set, such that if AI solves it in 2025 you’ll update significantly towards my position?
I would also venture to guess with less confidence that 1 and 3 might be because of this as well
Agreed, I do expect that the performance on all of those is mediated by the same variable(s); that’s why I called them a “cluster”.
benchmarks made by METR who was specifically trying to measure AI R&D ability and agency abilities, and which genuinely do seem to require (small) amounts of agency
I think “agency” is a bit of an overly abstract/confusing term to use, here. In particular, I think it also allows both a “top-down” and a “bottom-up” approach.
Humans have “bottom-up” agency: they’re engaging in fluid-intelligence problem-solving and end up “drawing” a decision-making pattern of a specific shape. An LLM, on this model, has a database of templates for such decision-making patterns, and it retrieves the best-fit agency template for whatever problem it’s facing. o1/RL-on-CoTs is a way to deliberately target the set of agency-templates an LLM has, extending it. But it doesn’t change the ultimate nature of what’s happening.
In particular: the bottom-up approach would allow an agent to stay on-target for an arbitrarily long time, creating an arbitrarily precise fit for whatever problem it’s facing. An LLM’s ability to stay on-target, however, would always remain limited by the length and the expressiveness of the templates that were trained into it.
RL on CoTs is a great way to further mask the problem, which is why the o-series seems to make unusual progress on agency-measuring benchmarks. But it’s still just masking it.
can you say what it is about LLM architecture and/or training methods that renders them incapable of thinking in the bottom-up way?
Not sure. I think it might be some combination of “the pretraining phase moves the model deep into the local-minimum abyss of top-down cognition, and the cheaper post-training phase can never hope to get it out of there” and “the LLM architecture sucks, actually”. But I would rather not get into the specifics.
Can you describe an intellectual or practical feat, or ideally a problem set, such that if AI solves it in 2025 you’ll update significantly towards my position?
“Inventing a new field of science” would do it, as far as more-or-less legible measures go. Anything less than that is too easily “fakeable” using top-down reasoning.
That said, I may make this update based on less legible vibes-based evidence, such as if o3′s advice on real-life problems seems to be unusually lucid and creative. (I’m tracking the possibility that LLMs are steadily growing in general capability and that they simply haven’t yet reached the level that impresses me personally. But on balance, I mostly don’t expect this possibility to be realized.)
“Inventing a new field of science” would do it, as far as more-or-less legible measures go. Anything less than that is too easily “fakeable” using top-down reasoning.
Seems unlikely we’ll see this before stuff gets seriously crazy on anyone’s views. (Has any new field of science been invented in the last 5 years by humans? I’m not sure what you’d count.)
It seems like we should at least update towards AIs being very useful for accelerating AI R&D if we very clearly see AI R&D greatly accelerate and it is using tons of AI labor. (And this was the initial top level prompt for this thread.) We could say something similar about other types of research.
Seems unlikely we’ll see this before stuff gets seriously crazy on anyone’s views. (Has any new field of science been invented in the last 5 years? I’m not sure what you’d count.)
Maybe some minor science fields, but yeah entirely new science fields in 5 years is deep into ASI territory, assuming it’s something like a hard science like physics.
(I’m tracking the possibility that LLMs are steadily growing in general capability and that they simply haven’t yet reached the level that impresses me personally. But on balance, I mostly don’t expect this possibility to be realized.)
That possibility is what I believe. I wish we had something to bet on better than “inventing a new field of science,” because by the time we observe that, there probably won’t be much time left to do anything about it. What about e.g. “I, Daniel Kokotajlo, are able to use AI agents basically as substitutes for human engineer/programmer employees. I, as a non-coder, can chat with them and describe ML experiments I want them to run or websites I want them to build etc., and they’ll make it happen at least as quickly and well as a competent professional would.” (And not just for simple websites, for the kind of experiments I’d want to run, which aren’t the most complicated but they aren’t that different from things actual AI company engineers would be doing.)
What about “The model is seemingly as good at solving math problems and puzzles as Thane is, not just on average across many problems but on pretty much any specific problem including on novel ones that are unfamiliar to both of you?
Humans have “bottom-up” agency: they’re engaging in fluid-intelligence problem-solving and end up “drawing” a decision-making pattern of a specific shape. An LLM, on this model, has a database of templates for such decision-making patterns, and it retrieves the best-fit agency template for whatever problem it’s facing. o1/RL-on-CoTs is a way to deliberately target the set of agency-templates an LLM has, extending it. But it doesn’t change the ultimate nature of what’s happening.
In particular: the bottom-up approach would allow an agent to stay on-target for an arbitrarily long time, creating an arbitrarily precise fit for whatever problem it’s facing. An LLM’s ability to stay on-target, however, would always remain limited by the length and the expressiveness of the templates that were trained into it.
Miscellaneous thoughts: I don’t yet buy that this distinction between top-down and bottom-up is binary, and insofar as it’s a spectrum then I’d be willing to bet that there’s been progress along it in recent years. Moreover I’m not even convinced that this distinction matters much for generalization radius / general intelligence, and it’s even less likely to matter for ‘ability to 5x AI R&D’ which is the milestone I’m trying to predict first. Moreover, I don’t think humans stay on-target for an arbitrarily long time.
I wish we had something to bet on better than “inventing a new field of science,”
I’ve thought of one potential observable that is concrete, should be relatively low-capability, and should provoke a strong update towards your model for me:
If there is an AI model such that the complexity of R&D problems it can solve (1) scales basically boundlessly with the amount of serial compute provided to it (or to a “research fleet” based on it), (2) scales much faster with serial compute than with parallel compute, and (3) the required amount of human attention (“babysitting”) is constant or grows very slowly with the amount of serial compute.
This attempts to directly get at the “autonomous self-correction” and “ability to think about R&D problems strategically” ideas.
I’ve not fully thought through all possible ways reality could Goodhart to this benchmark, i. e. “technically” pass it but in a way I find unconvincing. For example, if I failed to include the condition (2), o3 would have probably already “passed” it (since it potentially achieved better performance on ARC-AGI and FrontierMath by sampling thousands of CoTs then outputting the most frequent answer). There might be other loopholes like this...
But it currently seems reasonable and True-Name-y to me.
What about “Daniel Kokotajlo can feed it his docs about some prosaic ML alignment agenda (e.g. the faithful CoT stuff) and then it can autonomously go off and implement the agenda and come back to him with a writeup of the results and takeaways. While working on this, it gets to check in with Daniel once a day for a brief 20-minute chat conversation.”
Does that seem to you like it’ll come earlier, or later, than the milestone you describe?
Prooobably ~simultaneously, but I can maybe see it coming earlier and in a way that isn’t wholly convincing to me. In particular, it would still be a fixed-length task; much longer-length than what the contemporary models can reliably manage today, but still hackable using poorly-generalizing “agency templates” instead of fully general “compact generators of agenty behavior” (which I speculate humans to have and RL’d LLMs not to). It would be some evidence in favor of “AI can accelerate AI R&D”, but not necessarily “LLMs trained via SSL+RL are AGI-complete”.
Actually, I can also see it coming later. For example, some suppose that the capability researchers invent some method for reliably-and-indefinitely extending the amount of serial computations a reasoning model can productively make use of, but the compute or memory requirements grow very fast with the length of a CoT. Some fairly solid empirical evidence and theoretical arguments in favor of boundless scaling can appear quickly, well before the algorithms are made optimal enough to (1) handle weeks-long CoTs and/or (2) allow wide adoption (thus making it available to you).
I think the second scenario is more plausible, actually.
OK. Next question: Suppose that next year we get a nice result showing that there is a model with serial inference-time scaling across e.g. MATH + FrontierMath + IMO problems. Recall that FrontierMath and IMO are subdivided into different difficulty levels; suppose that this model can be given e.g. 10 tokens of CoT, 100, 1000, 10,000, etc. and then somewhere around the billion-serial-token-level it starts solving a decent chunk of the “medium” FrontierMath problems (but not all) and at the million-serial-token level it was only solving MATH + some easy IMO problems.
Not for math benchmarks. Here’s one way it can “cheat” at them: suppose that the CoT would involve the model generating candidate proofs/derivations, then running an internal (learned, not hard-coded) proof verifier on them, and either rejecting the candidate proof and trying to generate a new one, or outputting it. We know that this is possible, since we know that proof verifiers can be compactly specified.
This wouldn’t actually show “agency” and strategic thinking of the kinds that might generalize to open-ended domains and “true” long-horizon tasks. In particular, this would mostly fail the condition (2) from my previous comment.
Something more open-ended and requiring “research taste” would be needed. Maybe a comparable performance on METR’s benchmark would work for this (i. e., the model can beat a significantly larger fraction of it at 1 billion tokens compared to 1 million)? Or some other benchmark that comes closer to evaluating real-world performance.
Edit: Oh, math-benchmark performance would convince me if we get access to a CoT sample and it shows that the model doesn’t follow the above “cheating” approach, but instead approaches the problem strategically (in some sense). (Which would also require this CoT not to be hopelessly steganographied, obviously.)
Have you looked at samples of CoT of o1, o3, deepseek, etc. solving hard math problems? I feel like a few examples have been shown & they seem to involve qualitative thinking, not just brute-force-proof-search (though of course they show lots of failed attempts and backtracking—just like a human thought-chain would).
Anyhow, this is nice, because I do expect that probably something like this milestone will be reached before AGI (though I’m not sure)
Have you looked at samples of CoT of o1, o3, deepseek, etc. solving hard math problems?
Certainly (experimenting with r1′s CoTs right now, in fact). I agree that they’re not doing the brute-force stuff I mentioned; that was just me outlining a scenario in which a system “technically” clears the bar you’d outlined, yet I end up unmoved (I don’t want to end up goalpost-moving).
Though neither are they being “strategic” in the way I expect they’d need to be in order to productively use a billion-token CoT.
Anyhow, this is nice, because I do expect that probably something like this milestone will be reached before AGI
Yeah, I’m also glad to finally have something concrete-ish to watch out for. Thanks for prompting me!
Boy do I disagree with this take! Excited to discuss.
Can you say more about what skills you think the GPT series has shown ~0 improvement on?
Because if it’s “competent, autonomous agency” then there has been massive progress over the last two years and over the last few months in particular. METR has basically spent dozens of FTE-years specifically trying to measure progress in autonomous agency capability, both with formal benchmarks and with lots of high-surface-area interaction with models (they have people building scaffolds to make the AIs into agents and do various tasks etc.) And METR seems to think that progress has been rapid and indeed faster than they expected.
Has there been enough progress to automate swathes of jobs? No, of course not—see the benchmarks. E.g. RE-bench shows that even the best public models like o1 and newsonnet are only as good as professional coders on time horizons of, like, an hour or so. (give or take, depends on how you measure, the task, etc.) Which means that if you give them the sort of task that would take a normal employee, like, three hours, they are worse than a competent human professional. Specifically they’d burn lots of tokens and compute and push lots of buggy code and overall make a mess of things, just like an eager but incompetent employee.
And I’d say the models are unusually good at these coding tasks compared to other kinds of useful professional tasks, because the companies have been trying harder to train them to code and it’s inherently easier to train due to faster feedback loops etc.
Alright, let’s try this. But this is going to be vague.
Here’s a cluster of things that SotA AIs seem stubbornly bad at:
Innovation. LLMs are perfectly able to understand an innovative idea if it’s described to them, even if it’s a new idea that was produced after their knowledge-cutoff date. Yet, there hasn’t been a single LLM-originating innovation, and all attempts to design “AI scientists” have produced useless slop. They seem to have terrible “research taste”, even though they should be able to learn this implicit skill from the training data.
Reliability. Humans are very reliable agents, and SotA AIs aren’t, even when e. g. put into wrappers that encourage them to sanity-check their work. The gap in reliability seems qualitative, rather than just quantitative.
Solving non-templated problems. There seems to be a bimodal distribution of a sort, where some people report LLMs producing excellent code/math, and others report that they fail basic tasks.
Compounding returns on problem-solving time. As the graph you provided shows, humans’ performance scales dramatically with the time they spent on the problem, whereas AIs’ – even o1′s – doesn’t.
My sense is that LLMs are missing some sort of “self-steering” “true autonomy” quality; the quality that allows humans to:
Stare at the actual problem they’re solving, and build its highly detailed model in a “bottom-up” manner. Instead, LLMs go “top-down”: they retrieve the closest-match template problem from a vast database, fill-in some details, and solve that problem.
(Non-templatedness/fluid intelligence.)
Iteratively improve their model of a problem over the course of problem-solving, and do sophisticated course-correction if they realize their strategy isn’t working or if they’re solving the wrong problem. Humans can “snap out of it” if they realize they’re messing up, instead of just doing what they’re doing on inertia.
(Reliability.)
Recognize when their model of a given problem represents a nontrivially new “template” that can be memorized and applied in a variety of other situations, and what these situations might be.
(Innovation.)
My model is that all LLM progress so far has involved making LLMs better at the “top-down” thing. They end up with increasingly bigger databases of template problems, the closest-match templates end up ever-closer to the actual problems they’re facing, their ability to fill-in the details becomes ever-richer, etc. This improves their zero-shot skills, and test-time compute scaling allows them to “feel out” the problem’s shape over an extended period and find an ever-more-detailed top-down fit.
But it’s still fundamentally not what humans do. Humans are able to instantiate a completely new abstract model of a problem – even if it’s initially based on a stored template – and chisel at it until it matches the actual problem near-perfectly. This allows them to be much more reliable; this allows them to keep themselves on-track; this allows them to find “genuinely new” innovations.
The two methods do ultimately converge to the same end result: in the limit of a sufficiently expressive template-database, LLMs would be able to attain the same level of reliability/problem-representation-accuracy as humans. But the top-down method of approaching this limit seems ruinously computationally inefficient; perhaps so inefficient it saturates around GPT-4′s capability level.[1]
LLMs are sleep-walking. We can make their dreams ever-closer to reality, and that makes the illusion that they’re awake ever-stronger. But they’re not, and the current approaches may not be able to wake them up at all.
(As an abstract analogy: imagine that you need to color the space bounded by some 2D curve. In one case, you can take a pencil and do it directly. In another case, you have a collection of cutouts of geometric figures, and you have to fill the area by assembling a collage. If you have a sufficiently rich collection of figures, you can come arbitrarily close; but the “bottom-up” approach is strictly better. In particular, it can handle arbitrarily complicated shapes out-of-the-box, whereas the second approach would require dramatically bigger collections the more complicated the shapes get.)
Edit: Or so my current “bearish on LLMs” model goes. The performance of o3 or GPT-5/6 can very much break it, and the actual mechanisms described are necessarily speculative and tentative.
Under this toy model, it needn’t have saturated around this level; it could’ve comfortably overshot human capabilities. But this doesn’t seem to be what’s happening, likely due to some limitation of the current paradigm not covered by this model.
Thanks! Time will tell who is right. Point by point reply:
You list four things AIs seem stubbornly bad at: 1. Innovation. 2. Reliability. 3. Solving non-templated problems. 4. Compounding returns on problem-solving-time.
First of all, 2 and 4 seem closely related to me. I would say: “Agency skills” are the skills key to being an effective agent, i.e. skills useful for operating autonomously for long periods in pursuit of goals. Noticing when you are stuck is a simple example of an agency skill. Planning is another simple example. In-context learning is another example. would say that current AIs lack agency skills, and that 2 and 4 are just special cases of this. I would also venture to guess with less confidence that 1 and 3 might be because of this as well—perhaps the reason AIs haven’t made any truly novel innovations yet is that doing so takes intellectual work, work they can’t do because they can’t operate autonomously for long periods in pursuit of goals. (Note that reasoning models like o1 are a big leap in the direction of being able to do this!) And perhaps the reason behind the relatively poor performance on non-templated tasks is… wait actually no, that one has a very easy separate explanation, which is that they’ve been trained less on those tasks. A human, too, is better at stuff they’ve done a lot.
Secondly, and more importantly, I don’t think we can say there has been ~0 progress on these dimensions in the last few years, whether you conceive of them in your way or my way. Progress is in general s-curvy; adoption curves are s-curvy. Suppose for example that GPT2 was 4 SDs worse than average human at innovation, reliability, etc. and GPT3 was 3 SDs worse and GPT4 was 2 SDs worse and o1 is 1 SD worse. Under this supposition, the world would look the way that it looks today—Thane would notice zero novel innovations from AIs, Thane would have friends who try to use o1 for coding and find that it’s not useful without templates, etc. Meanwhile, as I’m sure you are aware pretty much every benchmark anyone has ever made has shown rapid progress in the last few years—including benchmarks made by METR who was specifically trying to measure AI R&D ability and agency abilities, and which genuinely do seem to require (small) amounts of agency. So I think the balance of evidence is in favor of progress on the dimensions you are talking about—it just hasn’t reached human level yet, or at any rate not the level at which you’d notice big exciting changes in the world. (Analogous to: Suppose we’ve measured COVID in some countries but not others, and found that in every country we’ve measured, COVID has spread to about 0.01% − 0.001% of the population, and is growing exponentially. If we live in a country that hasn’t measured yet, we should assume COVID is spreading even though we don’t know anyone personally who is sick yet.)
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You say:
Top down vs. bottom-up seem like two different ways of solving intellectual problems. Do you think it’s a sharp binary distinction? Or do you think it’s a spectrum? If the latter, what makes you think o1 isn’t farther along the spectrum than GPT3? If the former—if it’s a sharp binary—can you say what it is about LLM architecture and/or training methods that renders them incapable of thinking in the bottom-up way? (Like, naively it seems like o1 can do sophisticated reasoning. Moreover, it seems like it was trained in a way that would incentivize it to learn skills useful for solving math problems, and ‘bottom-up reasoning’ seems like a skill that would be useful. Why wouldn’t it learn it?)
Can you describe an intellectual or practical feat, or ideally a problem set, such that if AI solves it in 2025 you’ll update significantly towards my position?
Agreed, I do expect that the performance on all of those is mediated by the same variable(s); that’s why I called them a “cluster”.
I think “agency” is a bit of an overly abstract/confusing term to use, here. In particular, I think it also allows both a “top-down” and a “bottom-up” approach.
Humans have “bottom-up” agency: they’re engaging in fluid-intelligence problem-solving and end up “drawing” a decision-making pattern of a specific shape. An LLM, on this model, has a database of templates for such decision-making patterns, and it retrieves the best-fit agency template for whatever problem it’s facing. o1/RL-on-CoTs is a way to deliberately target the set of agency-templates an LLM has, extending it. But it doesn’t change the ultimate nature of what’s happening.
In particular: the bottom-up approach would allow an agent to stay on-target for an arbitrarily long time, creating an arbitrarily precise fit for whatever problem it’s facing. An LLM’s ability to stay on-target, however, would always remain limited by the length and the expressiveness of the templates that were trained into it.
RL on CoTs is a great way to further mask the problem, which is why the o-series seems to make unusual progress on agency-measuring benchmarks. But it’s still just masking it.
Not sure. I think it might be some combination of “the pretraining phase moves the model deep into the local-minimum abyss of top-down cognition, and the cheaper post-training phase can never hope to get it out of there” and “the LLM architecture sucks, actually”. But I would rather not get into the specifics.
“Inventing a new field of science” would do it, as far as more-or-less legible measures go. Anything less than that is too easily “fakeable” using top-down reasoning.
That said, I may make this update based on less legible vibes-based evidence, such as if o3′s advice on real-life problems seems to be unusually lucid and creative. (I’m tracking the possibility that LLMs are steadily growing in general capability and that they simply haven’t yet reached the level that impresses me personally. But on balance, I mostly don’t expect this possibility to be realized.)
Seems unlikely we’ll see this before stuff gets seriously crazy on anyone’s views. (Has any new field of science been invented in the last 5 years by humans? I’m not sure what you’d count.)
It seems like we should at least update towards AIs being very useful for accelerating AI R&D if we very clearly see AI R&D greatly accelerate and it is using tons of AI labor. (And this was the initial top level prompt for this thread.) We could say something similar about other types of research.
Maybe some minor science fields, but yeah entirely new science fields in 5 years is deep into ASI territory, assuming it’s something like a hard science like physics.
Minor would count.
Thanks for the reply.
That possibility is what I believe. I wish we had something to bet on better than “inventing a new field of science,” because by the time we observe that, there probably won’t be much time left to do anything about it. What about e.g. “I, Daniel Kokotajlo, are able to use AI agents basically as substitutes for human engineer/programmer employees. I, as a non-coder, can chat with them and describe ML experiments I want them to run or websites I want them to build etc., and they’ll make it happen at least as quickly and well as a competent professional would.” (And not just for simple websites, for the kind of experiments I’d want to run, which aren’t the most complicated but they aren’t that different from things actual AI company engineers would be doing.)
What about “The model is seemingly as good at solving math problems and puzzles as Thane is, not just on average across many problems but on pretty much any specific problem including on novel ones that are unfamiliar to both of you?
Miscellaneous thoughts: I don’t yet buy that this distinction between top-down and bottom-up is binary, and insofar as it’s a spectrum then I’d be willing to bet that there’s been progress along it in recent years. Moreover I’m not even convinced that this distinction matters much for generalization radius / general intelligence, and it’s even less likely to matter for ‘ability to 5x AI R&D’ which is the milestone I’m trying to predict first. Moreover, I don’t think humans stay on-target for an arbitrarily long time.
I’ve thought of one potential observable that is concrete, should be relatively low-capability, and should provoke a strong update towards your model for me:
If there is an AI model such that the complexity of R&D problems it can solve (1) scales basically boundlessly with the amount of serial compute provided to it (or to a “research fleet” based on it), (2) scales much faster with serial compute than with parallel compute, and (3) the required amount of human attention (“babysitting”) is constant or grows very slowly with the amount of serial compute.
This attempts to directly get at the “autonomous self-correction” and “ability to think about R&D problems strategically” ideas.
I’ve not fully thought through all possible ways reality could Goodhart to this benchmark, i. e. “technically” pass it but in a way I find unconvincing. For example, if I failed to include the condition (2), o3 would have probably already “passed” it (since it potentially achieved better performance on ARC-AGI and FrontierMath by sampling thousands of CoTs then outputting the most frequent answer). There might be other loopholes like this...
But it currently seems reasonable and True-Name-y to me.
Nice.
What about “Daniel Kokotajlo can feed it his docs about some prosaic ML alignment agenda (e.g. the faithful CoT stuff) and then it can autonomously go off and implement the agenda and come back to him with a writeup of the results and takeaways. While working on this, it gets to check in with Daniel once a day for a brief 20-minute chat conversation.”
Does that seem to you like it’ll come earlier, or later, than the milestone you describe?
Prooobably ~simultaneously, but I can maybe see it coming earlier and in a way that isn’t wholly convincing to me. In particular, it would still be a fixed-length task; much longer-length than what the contemporary models can reliably manage today, but still hackable using poorly-generalizing “agency templates” instead of fully general “compact generators of agenty behavior” (which I speculate humans to have and RL’d LLMs not to). It would be some evidence in favor of “AI can accelerate AI R&D”, but not necessarily “LLMs trained via SSL+RL are AGI-complete”.
Actually, I can also see it coming later. For example, some suppose that the capability researchers invent some method for reliably-and-indefinitely extending the amount of serial computations a reasoning model can productively make use of, but the compute or memory requirements grow very fast with the length of a CoT. Some fairly solid empirical evidence and theoretical arguments in favor of boundless scaling can appear quickly, well before the algorithms are made optimal enough to (1) handle weeks-long CoTs and/or (2) allow wide adoption (thus making it available to you).
I think the second scenario is more plausible, actually.
OK. Next question: Suppose that next year we get a nice result showing that there is a model with serial inference-time scaling across e.g. MATH + FrontierMath + IMO problems. Recall that FrontierMath and IMO are subdivided into different difficulty levels; suppose that this model can be given e.g. 10 tokens of CoT, 100, 1000, 10,000, etc. and then somewhere around the billion-serial-token-level it starts solving a decent chunk of the “medium” FrontierMath problems (but not all) and at the million-serial-token level it was only solving MATH + some easy IMO problems.
Would this count, for you?
Not for math benchmarks. Here’s one way it can “cheat” at them: suppose that the CoT would involve the model generating candidate proofs/derivations, then running an internal (learned, not hard-coded) proof verifier on them, and either rejecting the candidate proof and trying to generate a new one, or outputting it. We know that this is possible, since we know that proof verifiers can be compactly specified.
This wouldn’t actually show “agency” and strategic thinking of the kinds that might generalize to open-ended domains and “true” long-horizon tasks. In particular, this would mostly fail the condition (2) from my previous comment.
Something more open-ended and requiring “research taste” would be needed. Maybe a comparable performance on METR’s benchmark would work for this (i. e., the model can beat a significantly larger fraction of it at 1 billion tokens compared to 1 million)? Or some other benchmark that comes closer to evaluating real-world performance.
Edit: Oh, math-benchmark performance would convince me if we get access to a CoT sample and it shows that the model doesn’t follow the above “cheating” approach, but instead approaches the problem strategically (in some sense). (Which would also require this CoT not to be hopelessly steganographied, obviously.)
Have you looked at samples of CoT of o1, o3, deepseek, etc. solving hard math problems? I feel like a few examples have been shown & they seem to involve qualitative thinking, not just brute-force-proof-search (though of course they show lots of failed attempts and backtracking—just like a human thought-chain would).
Anyhow, this is nice, because I do expect that probably something like this milestone will be reached before AGI (though I’m not sure)
Certainly (experimenting with r1′s CoTs right now, in fact). I agree that they’re not doing the brute-force stuff I mentioned; that was just me outlining a scenario in which a system “technically” clears the bar you’d outlined, yet I end up unmoved (I don’t want to end up goalpost-moving).
Though neither are they being “strategic” in the way I expect they’d need to be in order to productively use a billion-token CoT.
Yeah, I’m also glad to finally have something concrete-ish to watch out for. Thanks for prompting me!